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\(\color{darkblue}{\textbf{Data Basics}}\)


\(\color{dodgerblue}{\textbf{Process}}\)


\(\color{dodgerblue}{\textbf{Analysis Hierarchy}}\)


\(\color{dodgerblue}{\textbf{Equity}}\)

  • Where does this data come from?
  • Why was this data collected?
  • How was this data generated?
  • Is this data demographically representative?
  • Who is included and who is excluded from this data?
  • Whose voices, lives, and experiences are missing?
  • How much can this data be disaggregated by race, gender, ethnicity, etc.?
  • Are the categories mutually exclusive and fully inclusive?
  • Are there “other” categories and, if so, who does that include?
  • Who stands to benefit from this data?
  • Who might be harmed by the collection or publication of this data?

(See more in Urban Institute’s Do No Harm Guide)


\(\color{dodgerblue}{\textbf{Troubleshooting}}\)

Issue Common Fix
Input errors Cleaning, omitting
Unrealistic observations Sanity checks, filter thresholds
Noise/measurement errors Averaging, interpolation, de-noising
Low/heterogenous density Spatio-temporal aggregation, re-zoning
Representativity/biases Normalization, acknowledgment, additional data collection

\(\color{darkblue}{\textbf{Data Wrangling}}\)


\(\color{dodgerblue}{\textbf{Basics}}\)

R

\(\color{dodgerblue}{\textbf{Other Langauges}}\)

SQL

XML


\(\color{dodgerblue}{\textbf{Data Sources}}\)

\(\color{darkblue}{\textbf{Visualization}}\)


\(\color{dodgerblue}{\textbf{R}}\)


\(\color{dodgerblue}{\textbf{Python}}\)

\(\color{darkblue}{\textbf{Analysis}}\)


\(\color{dodgerblue}{\textbf{R}}\)


\(\color{dodgerblue}{\textbf{Python}}\)